Mammalian Atg8 proteins regulate lysosome and autolysosome biogenesis through SNARE s
2019; Springer Nature; Volume: 38; Issue: 22 Linguagem: Inglês
10.15252/embj.2019101994
ISSN1460-2075
AutoresYuexi Gu, Yakubu Princely Abudu, Suresh Kumar, Bhawana Bissa, Seong Won Choi, Jingyue Jia, Michael Lazarou, Eeva‐Liisa Eskelinen, Terje Johansen, Vojo Deretić,
Tópico(s)Adenosine and Purinergic Signaling
ResumoArticle18 October 2019free access Source DataTransparent process Mammalian Atg8 proteins regulate lysosome and autolysosome biogenesis through SNAREs Yuexi Gu orcid.org/0000-0002-7234-1758 Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Yakubu Princely Abudu orcid.org/0000-0002-8798-270X Molecular Cancer Research Group, Institute of Medical Biology, University of Tromsø-The Arctic University of Norway, Tromsø, Norway Search for more papers by this author Suresh Kumar Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Bhawana Bissa Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Seong Won Choi Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Jingyue Jia orcid.org/0000-0002-1522-9612 Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Michael Lazarou orcid.org/0000-0003-2150-5545 Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Australia Search for more papers by this author Eeva-Liisa Eskelinen Institute of Biomedicine, University of Turku, Turku, Finland Search for more papers by this author Terje Johansen orcid.org/0000-0003-1451-9578 Molecular Cancer Research Group, Institute of Medical Biology, University of Tromsø-The Arctic University of Norway, Tromsø, Norway Search for more papers by this author Vojo Deretic Corresponding Author [email protected] orcid.org/0000-0002-3624-5208 Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Yuexi Gu orcid.org/0000-0002-7234-1758 Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Yakubu Princely Abudu orcid.org/0000-0002-8798-270X Molecular Cancer Research Group, Institute of Medical Biology, University of Tromsø-The Arctic University of Norway, Tromsø, Norway Search for more papers by this author Suresh Kumar Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Bhawana Bissa Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Seong Won Choi Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Jingyue Jia orcid.org/0000-0002-1522-9612 Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Michael Lazarou orcid.org/0000-0003-2150-5545 Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Australia Search for more papers by this author Eeva-Liisa Eskelinen Institute of Biomedicine, University of Turku, Turku, Finland Search for more papers by this author Terje Johansen orcid.org/0000-0003-1451-9578 Molecular Cancer Research Group, Institute of Medical Biology, University of Tromsø-The Arctic University of Norway, Tromsø, Norway Search for more papers by this author Vojo Deretic Corresponding Author [email protected] orcid.org/0000-0002-3624-5208 Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Search for more papers by this author Author Information Yuexi Gu1,2, Yakubu Princely Abudu3, Suresh Kumar1,2, Bhawana Bissa1,2, Seong Won Choi2, Jingyue Jia1,2, Michael Lazarou4, Eeva-Liisa Eskelinen5, Terje Johansen3 and Vojo Deretic *,1,2 1Autophagy, Inflammation and Metabolism (AIM) Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA 2Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA 3Molecular Cancer Research Group, Institute of Medical Biology, University of Tromsø-The Arctic University of Norway, Tromsø, Norway 4Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Australia 5Institute of Biomedicine, University of Turku, Turku, Finland *Corresponding author. Tel: +1 505 272 3814; E-mail: [email protected] EMBO J (2019)38:e101994https://doi.org/10.15252/embj.2019101994 PDFDownload PDF of article text and main figures.AM PDF Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Mammalian homologs of yeast Atg8 protein (mAtg8s) are important in autophagy, but their exact mode of action remains ill-defined. Syntaxin 17 (Stx17), a SNARE with major roles in autophagy, was recently shown to bind mAtg8s. Here, we identified LC3-interacting regions (LIRs) in several SNAREs that broaden the landscape of the mAtg8-SNARE interactions. We found that Syntaxin 16 (Stx16) and its cognate SNARE partners all have LIR motifs and bind mAtg8s. Knockout of Stx16 caused defects in lysosome biogenesis, whereas a Stx16 and Stx17 double knockout completely blocked autophagic flux and decreased mitophagy, pexophagy, xenophagy, and ribophagy. Mechanistic analyses revealed that mAtg8s and Stx16 control several properties of lysosomal compartments including their function as platforms for active mTOR. These findings reveal a broad direct interaction of mAtg8s with SNAREs with impact on membrane remodeling in eukaryotic cells and expand the roles of mAtg8s to lysosome biogenesis. Synopsis While LC3B and other mammalian Atg8 (mAtg8) proteins induce autophagy, it remains unclear whether they have a direct role in autophagosome membrane remodelling. Here the authors show that mAtg8s bind to SNAP receptor (SNARE) proteins that contain LC3-interacting regions (LIRs) and thereby control SNARE localization and autolysosomal biogenesis during autophagy. mAtg8s bind Syntaxin 16 and its cognate SNAREs, Vti1a and Syntaxin 6, via their LIRs. Double knockout of Syntaxin 16 and Syntaxin 17 inhibits diverse types of autophagy, including bulk autophagy, mitophagy, pexophagy and xenophagy. mAtg8 proteins regulate Syntaxin 16 localization and proper acidification of lysosomes. mAtg8s and Stx16 control recruitment of active mTOR to lysosomes. Introduction The autophagy pathway, controlled by a conserved set of ATG genes (Mizushima et al, 2011; Levine & Kroemer, 2019), is a cytoplasmic homeostatic process at an interface between quality control and metabolism. This pathway can be activated by metabolic and stress inputs (Noda & Ohsumi, 1998; Scott et al, 2004; Marino et al, 2014; Garcia & Shaw, 2017; Saxton & Sabatini, 2017) or triggered by cargo recognition leading to in situ assembly of ATG factors via receptor regulators (Mandell et al, 2014; Lazarou et al, 2015; Kimura et al, 2016). Autophagy can be bulk or selective (Birgisdottir et al, 2013; Randow & Youle, 2014; Stolz et al, 2014), whereas the multiple biological outputs of autophagy depend on its completion and the type of termination of the autophagic pathway, e.g., degradation or secretion (Ponpuak et al, 2015; Levine & Kroemer, 2019). Bulk autophagy, whereby portions of the cytoplasm of heterogeneous composition are sequestered, occurs during starvation and can be quantified by imaging (An & Harper, 2018) or biochemically (Seglen et al, 2015; Engedal & Seglen, 2016) by following sequestration of the cytosolic enzyme LDH, or ultrastructurally by enumerating partially degraded electron-dense ribosomes (Tanaka et al, 2000; Eskelinen, 2008). Autophagy can be selectively guided to specific cargo via a number of sequestosome 1/p62-like receptors (SLRs; Bjorkoy et al, 2005; Birgisdottir et al, 2013; Stolz et al, 2014) or other classes of receptors besides SLRs (Kimura et al, 2016; Levine & Kroemer, 2019). Autophagy machinery is often recruited to damaged or dysfunctional targets after they are marked for autophagic degradation by ubiquitin and galectin tags that in turn are recognized by cytosolic autophagic receptors such as SLRs (Randow & Youle, 2014; Stolz et al, 2014). Autophagy apparatus can also be directly recruited via receptors residing on or within organelles if they become exposed following organellar membrane damage or depolarization or are modified downstream of physiological or developmental signals (Sandoval et al, 2008; Wild et al, 2011; Liu et al, 2012; Bhujabal et al, 2017; Wei et al, 2017). Degradative autophagy terminates in fusion of autophagosomes with lysosomes whereby the sequestered cargo is degraded. Examples of selective degradative autophagy include autophagy of mitochondria (mitophagy), peroxisomes (pexophagy), intracellular microbes (xenophagy), ribosomes (ribophagy), protein aggregates (aggrephagy), and specific intracellular multiprotein complexes (precision autophagy) (Birgisdottir et al, 2013; Randow & Youle, 2014; Kimura et al, 2016; An & Harper, 2018). Given the diversity of cargo, complexity of the protein components and membrane compartments engaged, as well as the exquisite responsiveness to a variety of cargo triggers (e.g., damaged organelles, aggregates) and stress conditions (e.g., starvation, hypoxia), autophagy is controlled by a collection of subsystems that have to come together in a modular fashion and cooperate in the initiation and execution of autophagy (Mizushima et al, 2011). These include (i) formation of a complex between the ULK1 kinase, FIP200, ATG13, and ATG101, transducing mTOR inhibition (Ganley et al, 2009; Hosokawa et al, 2009; Jung et al, 2009), and AMPK activation (Kim et al, 2011) to induce autophagy; (ii) generation of PI3P by the ATG14-endowed class III PI3-kinase complex I (PI3KC3-CI) containing VPS34 (PI3KC3-C1) (Petiot et al, 2000; Baskaran et al, 2014; Chang et al, 2019) and Beclin 1 (He & Levine, 2010), which can also be modified by AMPK to specifically activate PI3KC3-C1 and not the other PI3KC3 forms (Kim et al, 2013) 11 Correction added on 24 October 2019, after first online publication: the sentence has been corrected. ; (iii) the ubiquitin-like conjugation system with ATG5-ATG12/ATG16L1 (Mizushima et al, 1998a,1998b) acting as an E3 ligase to lipidate mammalian homologs of yeast Atg8 (mAtg8s), some of which like LC3B have become key markers for autophagosomal membranes (Kabeya et al, 2000); and (iv) the only integral membrane ATG protein, ATG9, and the ATG2-WIPI protein complexes, of still unknown but essential functions (Young et al, 2006; Velikkakath et al, 2012; Bakula et al, 2017). These modules are for the most part interconnected, with FIP200 physically bridging via ATG16L1 the ULK1/2 complex with the mAtg8 conjugation system (Fujita et al, 2013; Gammoh et al, 2013; Nishimura et al, 2013), ATG16L1 and WIPI directly interacting (Dooley et al, 2014), ATG13 connecting the ULK1/2 complex with PI3C3-C1 via ATG13's HORMA domain binding to ATG14 of PI3KC3-C1 (Jao et al, 2013; Park et al, 2016), and PI3P, the product of VPS34 (Petiot et al, 2000), being detected on membranes by WIPIs (Bakula et al, 2017). In yeast, a component of the above systems morphologically equated with autophagy is Atg8, with its appearance as a single punctum defining the pre-autophagosomal structure (Kirisako et al, 1999). In mammals, this function is spread over a set of six mAtg8s (Weidberg et al, 2010; Mizushima et al, 2011), LC3A, LC3B, LC3C, GABARAP, GABARAPL1, and GABARAPL2. Although it is generally accepted that mAtg8s are important for autophagy, and that mAtg8 lipidation and puncta formation herald autophagy induction and isolation membrane formation, the core function of mAtg8s and possibly a direct role in autophagosomal membrane remodeling remains to be defined. Several models, including a direct role of Atg8/mAtg8s in catalyzing membrane fusion (Nakatogawa et al, 2007; Weidberg et al, 2010, 2011), have been considered but later challenged (Nair et al, 2011). Nevertheless, many of the cargos, selective autophagy receptors, and regulatory components (ULK, ATG13, FIP200, ATG14, Beclin 1, VPS34, ATG4) contain LC3-interacting regions (LIRs) and can either recruit cargo (Bjorkoy et al, 2005; Birgisdottir et al, 2013) or organize core autophagy machinery (Alemu et al, 2012; Skytte Rasmussen et al, 2017; Birgisdottir et al, 2019). Furthermore, recent studies show that even in the complete absence of mAtg8s, autophagosome formation proceeds, albeit with a lower sequestration volume, and that mAtg8s are important for autolysosomal formation (Nguyen et al, 2016a). In keeping with the latter findings, depletion of components participating in mAtg8 conjugation delays autophagic flux progression (Tsuboyama et al, 2016). It remains to be understood precisely how mAtg8s control membrane trafficking and remodeling during phagophore formation and elongation (Xie et al, 2008) and during autophagosome–lysosome fusion (Weidberg et al, 2010; Nguyen et al, 2016a). Degradative aspects of autophagy depend on autophagosome fusion with organelles of the endolysosomal system, often referred to as autophagic flux or autophagosome–lysosome fusion (Tanaka et al, 2000; Klionsky et al, 2016). Much of the intracellular membrane trafficking and fusion processes are catalyzed by SNARE proteins, which ensure compartment/organelle specificity through pairing of cognate Qa-, Qb-, Qc-, and R-SNARE partners (Jahn & Scheller, 2006). SNAREs are often found in compartments where they function but also transit through other membranes, and thus, their fusion activities are tightly regulated by tethers, SM (Sec1/Munc18) proteins, Rabs, and additional factors that modulate their recruitment, activation, cycles of reuse, etc. (Hong & Lev, 2014). In the context of autophagy, SNAREs have been studied at different stages along the autophagy pathway (Nair et al, 2011; Itakura et al, 2012; Moreau et al, 2013; Kimura et al, 2017). At the autophagosome–lysosome fusion stage, an initially identified SNARE was the Qa-SNARE Stx17, forming complexes with Qbc-SNARE SNAP29 and R-SNARE VAMP8 (Itakura et al, 2012; Takats et al, 2013; Guo et al, 2014; Diao et al, 2015; Wang et al, 2016). Recent studies have indicated that additional SNAREs may be required or even be dominant in this process (Matsui et al, 2018; Takats et al, 2018). Thus, redundant, complementary or synergistic SNARE-driven fusion and regulatory events may be at work during autolysosomal fusion. We have recently shown that Stx17 interacts via its LIR motif with mAtg8s, which functions in Stx17's recruitment to autophagosomes (Kumar et al, 2018), and that TBK1-phosphorylated Stx17 plays a role in autophagy initiation (Kumar et al, 2019), in keeping with studies by others (Hamasaki et al, 2013; Arasaki et al, 2015, 2018). Thus, Stx17 contributes to the autophagy pathway at several stages from initiation to maturation. A report of direct interaction between mAtg8s and Stx17 (Kumar et al, 2018) has set an unanticipated precedent, and here, we explored whether there is a broader range of interactions between mAtg8s and mammalian SNAREs. Using bioinformatics and biochemical approaches, we found additional candidate SNAREs with LIR motifs that bind mAtg8s. Among the candidates, we focused on a cognate set of SNAREs, Stx16, Vti1a, and Stx6, previously implicated in retrograde transport between endosomes and the trans-Golgi network (TGN). Through mutational and functional analyses, we found that Stx16 acts synergistically with Stx17 and is important for diverse types of autophagy, including mitophagy, pexophagy, ribophagy, and elimination of intracellular Mycobacterium tuberculosis. We have further found that Stx16 is important for cellular lysosomal content and function, and that mAtg8s modulate Stx16 localization on endolysosomal organelles. This uncovers a mechanism different from the previous views of how mAtg8s help grow autophagosomal and complete autolysosomal membranes. We conclude that mAtg8s control autophagy at least in part by directly binding to SNARE proteins engaged in the biogenesis of the endolysosomal and autolysosomal organelles. Results A subset of SNAREs including syntaxin 16 contain LC3-interacting regions Previous studies have indicated that at least one SNARE involved in autophagy, Stx17, harbors a LIR motif that affects its distribution (Kumar et al, 2018). Here, we carried out a broader analysis of all SNAREs for the presence of putative LIR motifs using bioinformatics and conventional LIR motif consensus (Birgisdottir et al, 2013) followed up by biochemical assays (Fig 1A and Appendix Table S1). We tested a subset of SNAREs containing putative LIRs in a screen with peptide arrays for binding to GST-GABARAP as a representative mAtg8 (Fig 1A). Several SNAREs showed positive signals with GST-GABARAP in peptide arrays. These included only one R-SNARE, VAMP7, and mostly Qa-, Qb-, and Qc-SNAREs acting in different cellular compartments (Jahn & Scheller, 2006): Stx17, included as a positive control (Kumar et al, 2018) and already implicated in autophagy (Itakura et al, 2012), Stx18 acting in the ER (Hatsuzawa et al, 2000), GOSR1 functioning in the Golgi (Subramaniam et al, 1996; Mallard et al, 2002), Stx3 and Stx4 on plasma membrane (Low et al, 1996), Stx19 with no transmembrane domain and partitioning between cytosol and membranes (Wang et al, 2006), and Stx16, Stx6, and Vti1a, all acting in trafficking between endosomal compartments and TGN (Jahn & Scheller, 2006; Malsam & Söllner, 2011) (Figs 1B and EV1A). Thus, a number of SNAREs may be binding partners for mAtg8s indicating a previously unappreciated potential for broader intersections between mAtg8 and SNARE systems. Figure 1. Many SNAREs bind mammalian Atg8 proteins Peptide array dot blot analysis to identify LIR motifs in the indicated SNARE proteins. Amino acids for the identified LIRs from positive signals are marked on each SNARE. Amino acids are denoted as amino acid single letter codes in the blot. Schematics of the functional domains of Stx16/Vti1a/Stx6 cognate SNAREs with the positions of LIR motifs. The LIR motif marked in green in syntaxin 16 indicates established LIR based on following analyses. Co-immunoprecipitation (Co-IP) analysis of interactions between FLAG-tagged Stx16/Vti1a/Stx6 and EGFP-tagged LC3B or GABARAP overexpressed in HEK293T cells. * indicates mouse IgG heavy chain of precipitated mouse anti-FLAG antibody. GST pull-down analysis of interactions between radiolabeled Myc-Stx16 and GST-tagged mAtg8 proteins. GST pull-down analysis of interactions between wild-type (WT) or LIR-mutant (L219A/V222A) Stx16 and GST-tagged LC3C or GABARAP. Lower panel shows percentages of WT or LIR-mutant Stx16 bound to GST-LC3C or GST-GABARAP. Data shown as means ± SEM of precipitated Stx16, n = 3. GST pull-down analysis of interactions between WT or different types of LIR-mutant Stx16 and GST-tagged LC3C or GABARAP. Lower panel shows percentages of WT or LIR-mutant Stx16 bound to GST-LC3C or GST-GABARAP. GST pull-down analysis of interactions between radiolabeled Myc-Stx16 and WT or LDS-mutant GABARAP (GABARAP-Y49A and -Y49A/F104A). Lower panel shows percentages of Stx16 bound to WT or LDS-mutant GABARAP. Data shown as means ± SEM of precipitated Stx16, n = 3. Source data are available online for this figure. Source Data for Figure 1 [embj2019101994-sup-0003-SDataFig1.pdf] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Peptide arrays identify a set of SNAREs bearing LIR motifs Schematics of the functional domains of SNAREs bearing LIR motifs identified through bioinformatic and peptide array analyses. LIR motifs with the amino acid sequences are marked as red bars in approximate positions on each SNARE protein. The LIR motifs in Stx16 and Stx17 are marked green as they are validated LIRs through biochemical and functional analyses. Endogenous Co-IP analysis of the interactions between LC3 and Stx16, Vti1a in HeLa and U2OS cells. * indicates IgG heavy chain of precipitated anti-LC3 antibody. Source data are available online for this figure. Download figure Download PowerPoint Syntaxin 16 directly binds mAtg8s through its LIR motif From the panel of SNAREs showing positive signals with GST-GABARAP in peptide arrays, we focused on a set of three cognate SNAREs, Stx16, Vti1a, and Stx6, known to form a complex (Malsam & Söllner, 2011). Co-immunoprecipitation (Co-IP) analyses of overexpressed fusion proteins confirmed that Stx16, Vti1a, and Stx6 interact with LC3B and GABARAP (Fig 1C). Co-IP analyses of endogenous proteins in HeLa and U2OS cells confirmed interactions between LC3 and Stx16 and Vti1a (Fig EV1B). In GST pull-down assays with all 6 mAtg8 proteins, Stx16 showed a positive binding signal with LC3C, GABARAP, and GABARAPL1 (Fig 1D). The peptide array binding results pointed to the 219LVLV222 residues in Stx16 (Fig 1B), which resembled both the LC3C-preferring LIR of NDP52 (ILVV; von Muhlinen et al, 2012) and the GABARAP interaction motifs with important Val residues in the second and fourth positions of the core LIR motif (Rogov et al, 2017). This motif was found in the linker region between the Habc and SNARE domains of Stx16 matching the requirement to be outside of known structured regions (Popelka & Klionsky, 2015). When we mutated L-219 and V-222 in the predicted (albeit atypical) LIR motif (219LVLV222 mutated to 219AVLA222) of Stx16, this reduced Stx16 binding to LC3C and GABARAP (Fig 1E). Additionally, we changed both Val residues within the 219LVLV222 motif to Ala, and the resulting Stx16 variant showed reduction in binding to LC3C and GABARAP comparable to the 219AVLA222 mutant in GST pull-down experiments (Fig 1F). Finally, when we mutated all 4 residues within the 219LVLV222 motif, the association between Stx16 and LC3C or GABARAP was completely abolished (Fig 1F). Complementary to the above experiments with LIR mutations, we tested whether the previously defined LIR-docking site (LDS) in GABARAP (Behrends et al, 2010; Birgisdottir et al, 2013) was responsible for binding to Stx16. Two conserved hydrophobic pockets (HP1 and HP2) are known to define the LIR-docking site of GABARAP (Behrends et al, 2010). The Y49A mutation in the HP1 pocket of GABARAP reduced its interaction with Stx16, whereas the complete LDS mutant (double HP1 and HP2 Y49A/F104A mutation) abrogated GABARAP's binding to Stx16 in GST pull-down assays (Fig 1G). Thus, GABARAP and Stx16 interaction depends on the canonical LDS motif in GABARAP. Syntaxin 16 is required for efficient autophagy flux in response to starvation Stx17 has been implicated in mammalian autophagosome–lysosome fusion (Itakura et al, 2012). However, additional SNAREs have now been shown to contribute to this process (Matsui et al, 2018), but relative contributions remain to be fully defined. We generated CRISPR/Cas9-mediated STX17 knockout (KO) in different cell lines (HeLa and Huh7; Fig EV2A and B) and analyzed autophagy flux by monitoring LC3-II levels (Figs 2A and B, and EV2C and D). In STX17KO HeLa cells, the autophagic flux continued at almost the same rates as in wild-type (WT) control cells, as shown by slight increase in LC3-II levels upon starvation (Fig 2A). A similar effect was observed in hepatocellular carcinoma Huh7 STX17KO cells, which in general displayed faster LC3-II flux (Fig EV2C and D). Knocking out Stx17 did not cause any significant change in the levels of p62, an autophagic receptor that is degraded upon starvation (Bjorkoy et al, 2005), and is often used as readout of autophagic flux (Klionsky et al, 2016; Figs 2A and EV2C). Click here to expand this figure. Figure EV2. Stx16 and Stx17 are required for efficient autophagic flux induced by starvation Sequences of guide RNAs (gRNA) targeting STX17 and STX16. Protospacer adjacent motif (PAM) sequences are shown in orange on the 3′ side of each gRNA. The exon numbers targeted by the gRNAs are shown below each gRNA. Validation of STX16 KO, STX17 KO, and STX16/STX17 DKO by Western blot analysis in HeLa and Huh7 cell lines. WT or STX17KO Huh7 cells were starved with or without the presence of bafilomycin A1 (Baf A1, 100 nM) for 2 h, and cell lysates were subjected to Western blot analysis of LC3B and p62. Quantifications of LC3B-II levels normalized to β-actin from cells treated as in (C). Data shown as means ± SEM of LC3B-II and β-actin ratios, n = 3; †, not significant (one-way ANOVA). STX16/STX17DKO HeLa cells were transfected with 3XFLAG-STX16, starved with or without the presence of Baf A1 for 2 h, and cell lysates were subjected to Western blot analysis of LC3B. Numbers below the panels are the average of LC3B-II/β-actin ratios normalized to the third lane (HeLa WT treated with EBSS plus Baf A1) from two independent experiments. WT or STX16/STX17DKO HeLa cells were transfected with mCherry-EYFP-GABARAP (tandem GABARAP), starved in EBSS or EBSS plus Baf A1 for 2 h, and subjected to high-content microscopic (HCM) analysis of autophagic flux for tandem GABARAP. Data shown as means ± SEM of the overlap area per cell between mCherry and EYFP, minimum 200 transfected cells were counted each well from at least 12 wells, three independent experiments; *P < 0.05; **P < 0.01 (two-way ANOVA). Representative images of tandem GABARAP transfected into WT or STX16/STX17DKO HeLa cells treated as in (F). Masks: white, cells successfully transfected with tandem GABARAP identified based on average intensity of mCherry; red, mCherry puncta in transfected cells; green, EYFP puncta in transfected cells; yellow, overlap area between mCherry and EYFP. Scale bar: 20 μm. Source data are available online for this figure. Download figure Download PowerPoint Figure 2. The mAtg8-interacting SNAREs Stx16 and Stx17 are required for efficient bulk autophagic flux WT or STX17 knockout (STX17KO) HeLa cells were starved with or without the presence of bafilomycin A1 (Baf A1, 100 nM) for 2 h, and cell lysates were subjected to Western blot analysis of LC3B and p62. Quantifications of LC3B-II levels normalized to β-actin from cells treated as in (A); data shown as means ± SEM of LC3B-II and β-actin ratios, n = 3; *P < 0.05 (one-way ANOVA). WT, STX16KO, STX17KO, or STX16/STX17 double KO (STX16/STX17DKO) HeLa cells were starved with or without the presence of Baf A1 (100 nM) for 2 h, and cell lysates were subjected to Western blot analysis of LC3B. Quantifications of LC3B-II levels normalized to β-actin from (C); data shown as means ± SEM of LC3B-II and β-actin ratios, n = 3; †, not significant; **P < 0.01 (one-way ANOVA). WT or STX16/STX17DKO HeLa cells were starved in EBSS for 2 h and subjected to ultrastructural analysis of the autophagic vesicles (AV) with electron microscopy. AVi: initial autophagic vacuoles; AVd: degradative autophagic vacuoles; G: Golgi apparatus. Image acquisition and counting as in Methods. Scale bars, 1 μm and 0.5 μm (top sections). Quantifications of autophagic vesicles in WT and STX16/STX17DKO HeLa cells treated as (E); data shown as means ± SEM of AV profiles relative to cytoplasmic area; †, not significant; *P < 0.05 (two-way ANOVA). AV profiles from 47 images of each sample were counted. Source data are available online for this figure. Source Data for Figure 2 [embj2019101994-sup-0004-SDataFig2.pdf] Download figure Download PowerPoint When we knocked out STX16 in HeLa cells, this too had no majo
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